This note documents the data and programs used in the paper ��Estimation of a Generalized Fishery Model: A Two-Stage Approach�� by J. Zhang and M.D. Smith.

1 Data

The coastal reef-fish fishery data in the northeastern Gulf of Mexico is maintained by the Southeast Fisheries Science Center.  However, raw data cannot be disclosed legally for posting online. Data are protected under NOAA Administrative Order 216-100 (PROTECTION OF CONFIDENTIAL FISHERIES STATISTICS). Other researchers can gain access to the raw data by entering into an AGREEMENT OF ACCESS with the Southeast Fisheries Science Center. An AGREEMENT OF ACCESS details the legal conditions for use of raw data including appropriate storage, and the agreement requires signed certificates of non-disclosure from all individuals having access to the data.

2 Programs

2.1 Estimation

--��data_main.sas�� is a program written in SAS, which generates the data for regression from the raw logbook data. The following variables are used in this program:

-��schedule��: a unique serial number for each fishing trip,

-��species��: 62 reef-fish species are used, each of which has a unique 4-digit code,

-��area��: the area where a vessel goes fishing which is coded from 1 to 13,

-��started��: the starting date of a fishing trip,

-��landed��: the landing date of a fishing trip,

-��totlbs��: total pounds of harvest per trip, and

-��crew��: number of crew members.

This program generates four data sets:

-��reef_area.txt��: the full data set for the generalized Schaefer regression,

-��reef.txt��: the same as ��reef_area.txt�� but no area dummies,

-��schaefer.txt��: the data set for the Schaefer regression, and

-��tripnum.txt��: monthly total catch and effort information.

--��estimation_main.m�� is the main program written in Matlab for the regression, which calls the following functions:

-��hdrload.m�� loads data from an ASCII file containing a text header,

-��bstrag.m�� conducts bootstrapping in a grouped sample,

-��est2stage.m�� is the function that runs the two-step regression, and

-��linreg.m�� is a program for the OLS regression.

2.2 Monte Carlo Simulations

The folder ��monte-carlo�� contains three Monte Carlo experiments. Note that programs in this folder may call functions from the folder ��estimation��.

-��sample_size_simulation.m�� investigates how the sample size affects the performance of the two-stage estimator.  Its result is reported in Table 1 in the paper.  The program calls the function ��simu.m�� which generates the simulated data sets.

-��gsSimu.m�� compares the performance of the Schaefer and the generalized Schaefer estimators.  In the folder ��par1��, it is assumed that the data generation process (DGP) is the generalized Schaefer model.  In the folder ��par2��, the DGP is the Schaefer model.  The comparison results are not reported in the paper but are included in ��appendix.pdf��.

2.3 Analysis

--��summary_stat.sas�� provides summary statistics for the variables used in the regression.

--��analysis.m�� plots Figure 2 and 3 in the paper.


